Overview

Dataset statistics

Number of variables20
Number of observations2644
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory413.2 KiB
Average record size in memory160.0 B

Variable types

Numeric20

Alerts

Life expectancy is highly correlated with Adult Mortality and 11 other fieldsHigh correlation
Adult Mortality is highly correlated with Life expectancy and 2 other fieldsHigh correlation
infant deaths is highly correlated with Life expectancy and 6 other fieldsHigh correlation
percentage expenditure is highly correlated with GDP and 2 other fieldsHigh correlation
Hepatitis B is highly correlated with Polio and 1 other fieldsHigh correlation
Measles is highly correlated with infant deaths and 1 other fieldsHigh correlation
BMI is highly correlated with Life expectancy and 7 other fieldsHigh correlation
under-five deaths is highly correlated with Life expectancy and 6 other fieldsHigh correlation
Polio is highly correlated with Life expectancy and 3 other fieldsHigh correlation
Diphtheria is highly correlated with Life expectancy and 4 other fieldsHigh correlation
HIV/AIDS is highly correlated with Life expectancy and 7 other fieldsHigh correlation
GDP is highly correlated with Life expectancy and 3 other fieldsHigh correlation
thinness 10-19 years is highly correlated with Life expectancy and 3 other fieldsHigh correlation
thinness 5-9 years is highly correlated with Life expectancy and 3 other fieldsHigh correlation
Income composition of resources is highly correlated with Life expectancy and 11 other fieldsHigh correlation
Schooling is highly correlated with Life expectancy and 8 other fieldsHigh correlation
Life expectancy is highly correlated with Adult Mortality and 3 other fieldsHigh correlation
Adult Mortality is highly correlated with Life expectancy and 1 other fieldsHigh correlation
infant deaths is highly correlated with Measles and 2 other fieldsHigh correlation
percentage expenditure is highly correlated with GDPHigh correlation
Hepatitis B is highly correlated with DiphtheriaHigh correlation
Measles is highly correlated with infant deaths and 1 other fieldsHigh correlation
BMI is highly correlated with Life expectancy and 1 other fieldsHigh correlation
under-five deaths is highly correlated with infant deaths and 2 other fieldsHigh correlation
Polio is highly correlated with DiphtheriaHigh correlation
Diphtheria is highly correlated with Hepatitis B and 1 other fieldsHigh correlation
HIV/AIDS is highly correlated with Adult MortalityHigh correlation
GDP is highly correlated with percentage expenditureHigh correlation
Population is highly correlated with infant deaths and 1 other fieldsHigh correlation
thinness 10-19 years is highly correlated with thinness 5-9 yearsHigh correlation
thinness 5-9 years is highly correlated with thinness 10-19 yearsHigh correlation
Income composition of resources is highly correlated with Life expectancy and 1 other fieldsHigh correlation
Schooling is highly correlated with Life expectancy and 2 other fieldsHigh correlation
Life expectancy is highly correlated with Adult Mortality and 3 other fieldsHigh correlation
Adult Mortality is highly correlated with Life expectancyHigh correlation
infant deaths is highly correlated with under-five deathsHigh correlation
percentage expenditure is highly correlated with GDPHigh correlation
Hepatitis B is highly correlated with Polio and 1 other fieldsHigh correlation
under-five deaths is highly correlated with infant deathsHigh correlation
Polio is highly correlated with Hepatitis B and 1 other fieldsHigh correlation
Diphtheria is highly correlated with Hepatitis B and 1 other fieldsHigh correlation
HIV/AIDS is highly correlated with Life expectancyHigh correlation
GDP is highly correlated with percentage expenditureHigh correlation
thinness 10-19 years is highly correlated with thinness 5-9 yearsHigh correlation
thinness 5-9 years is highly correlated with thinness 10-19 yearsHigh correlation
Income composition of resources is highly correlated with Life expectancy and 1 other fieldsHigh correlation
Schooling is highly correlated with Life expectancy and 1 other fieldsHigh correlation
Unnamed: 0 is highly correlated with thinness 10-19 yearsHigh correlation
Life expectancy is highly correlated with Adult Mortality and 5 other fieldsHigh correlation
Adult Mortality is highly correlated with Life expectancy and 7 other fieldsHigh correlation
infant deaths is highly correlated with Measles and 4 other fieldsHigh correlation
Alcohol is highly correlated with BMI and 1 other fieldsHigh correlation
percentage expenditure is highly correlated with GDP and 1 other fieldsHigh correlation
Hepatitis B is highly correlated with Polio and 2 other fieldsHigh correlation
Measles is highly correlated with infant deaths and 1 other fieldsHigh correlation
BMI is highly correlated with Life expectancy and 9 other fieldsHigh correlation
under-five deaths is highly correlated with infant deaths and 4 other fieldsHigh correlation
Polio is highly correlated with Life expectancy and 5 other fieldsHigh correlation
Total expenditure is highly correlated with BMI and 1 other fieldsHigh correlation
Diphtheria is highly correlated with Life expectancy and 6 other fieldsHigh correlation
HIV/AIDS is highly correlated with Adult MortalityHigh correlation
GDP is highly correlated with percentage expenditure and 1 other fieldsHigh correlation
Population is highly correlated with infant deaths and 2 other fieldsHigh correlation
thinness 10-19 years is highly correlated with Unnamed: 0 and 8 other fieldsHigh correlation
thinness 5-9 years is highly correlated with infant deaths and 4 other fieldsHigh correlation
Income composition of resources is highly correlated with Life expectancy and 7 other fieldsHigh correlation
Schooling is highly correlated with Life expectancy and 10 other fieldsHigh correlation
Unnamed: 0 is uniformly distributed Uniform
Unnamed: 0 has unique values Unique
infant deaths has 803 (30.4%) zeros Zeros
Alcohol has 177 (6.7%) zeros Zeros
percentage expenditure has 498 (18.8%) zeros Zeros
Hepatitis B has 519 (19.6%) zeros Zeros
Measles has 899 (34.0%) zeros Zeros
BMI has 34 (1.3%) zeros Zeros
under-five deaths has 747 (28.3%) zeros Zeros
Total expenditure has 208 (7.9%) zeros Zeros
GDP has 352 (13.3%) zeros Zeros
Population has 540 (20.4%) zeros Zeros
thinness 10-19 years has 34 (1.3%) zeros Zeros
thinness 5-9 years has 34 (1.3%) zeros Zeros
Income composition of resources has 230 (8.7%) zeros Zeros
Schooling has 141 (5.3%) zeros Zeros

Reproduction

Analysis started2022-05-02 01:27:08.025715
Analysis finished2022-05-02 01:28:00.076436
Duration52.05 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct2644
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1321.5
Minimum0
Maximum2643
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size20.8 KiB
2022-05-01T20:28:00.172100image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile132.15
Q1660.75
median1321.5
Q31982.25
95-th percentile2510.85
Maximum2643
Range2643
Interquartile range (IQR)1321.5

Descriptive statistics

Standard deviation763.4013798
Coefficient of variation (CV)0.5776779264
Kurtosis-1.2
Mean1321.5
Median Absolute Deviation (MAD)661
Skewness0
Sum3494046
Variance582781.6667
MonotonicityStrictly increasing
2022-05-01T20:28:00.301651image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
17571
 
< 0.1%
17591
 
< 0.1%
17601
 
< 0.1%
17611
 
< 0.1%
17621
 
< 0.1%
17631
 
< 0.1%
17641
 
< 0.1%
17651
 
< 0.1%
17661
 
< 0.1%
Other values (2634)2634
99.6%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
26431
< 0.1%
26421
< 0.1%
26411
< 0.1%
26401
< 0.1%
26391
< 0.1%
26381
< 0.1%
26371
< 0.1%
26361
< 0.1%
26351
< 0.1%
26341
< 0.1%

Life expectancy
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct359
Distinct (%)13.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.06879728
Minimum0
Maximum89
Zeros9
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size20.8 KiB
2022-05-01T20:28:00.421476image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile51.4
Q162.975
median72.1
Q375.8
95-th percentile82
Maximum89
Range89
Interquartile range (IQR)12.825

Descriptive statistics

Standard deviation10.35175726
Coefficient of variation (CV)0.1498760319
Kurtosis5.676098829
Mean69.06879728
Median Absolute Deviation (MAD)5.9
Skewness-1.429161629
Sum182617.9
Variance107.1588785
MonotonicityNot monotonic
2022-05-01T20:28:00.533059image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7344
 
1.7%
7530
 
1.1%
7826
 
1.0%
73.624
 
0.9%
8123
 
0.9%
73.923
 
0.9%
74.522
 
0.8%
7622
 
0.8%
74.122
 
0.8%
73.521
 
0.8%
Other values (349)2387
90.3%
ValueCountFrequency (%)
09
0.3%
36.31
 
< 0.1%
391
 
< 0.1%
411
 
< 0.1%
41.51
 
< 0.1%
42.31
 
< 0.1%
43.11
 
< 0.1%
43.31
 
< 0.1%
43.51
 
< 0.1%
441
 
< 0.1%
ValueCountFrequency (%)
8911
0.4%
8810
0.4%
878
0.3%
8613
0.5%
8512
0.5%
8411
0.4%
83.71
 
< 0.1%
83.52
 
0.1%
83.41
 
< 0.1%
83.31
 
< 0.1%

Adult Mortality
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct409
Distinct (%)15.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean162.4652042
Minimum0
Maximum699
Zeros9
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size20.8 KiB
2022-05-01T20:28:00.658510image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12
Q173
median143
Q3226.25
95-th percentile392.7
Maximum699
Range699
Interquartile range (IQR)153.25

Descriptive statistics

Standard deviation121.1869533
Coefficient of variation (CV)0.7459255896
Kurtosis1.407400036
Mean162.4652042
Median Absolute Deviation (MAD)76
Skewness1.082963128
Sum429558
Variance14686.27764
MonotonicityNot monotonic
2022-05-01T20:28:00.785567image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1228
 
1.1%
1426
 
1.0%
1625
 
0.9%
1122
 
0.8%
14422
 
0.8%
13822
 
0.8%
1920
 
0.8%
7619
 
0.7%
1519
 
0.7%
12719
 
0.7%
Other values (399)2422
91.6%
ValueCountFrequency (%)
09
0.3%
111
0.4%
28
0.3%
36
 
0.2%
44
 
0.2%
52
 
0.1%
613
0.5%
715
0.6%
810
0.4%
912
0.5%
ValueCountFrequency (%)
6991
< 0.1%
6931
< 0.1%
6821
< 0.1%
6791
< 0.1%
6751
< 0.1%
6661
< 0.1%
6541
< 0.1%
6521
< 0.1%
6481
< 0.1%
6471
< 0.1%

infant deaths
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct197
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.14409985
Minimum0
Maximum1800
Zeros803
Zeros (%)30.4%
Negative0
Negative (%)0.0%
Memory size20.8 KiB
2022-05-01T20:28:00.908856image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q318
95-th percentile95
Maximum1800
Range1800
Interquartile range (IQR)18

Descriptive statistics

Standard deviation123.9323197
Coefficient of variation (CV)3.979319367
Kurtosis105.2156075
Mean31.14409985
Median Absolute Deviation (MAD)2
Skewness9.349131179
Sum82345
Variance15359.21986
MonotonicityNot monotonic
2022-05-01T20:28:01.022222image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0803
30.4%
1317
 
12.0%
2203
 
7.7%
3157
 
5.9%
479
 
3.0%
857
 
2.2%
1044
 
1.7%
744
 
1.7%
937
 
1.4%
1136
 
1.4%
Other values (187)867
32.8%
ValueCountFrequency (%)
0803
30.4%
1317
 
12.0%
2203
 
7.7%
3157
 
5.9%
479
 
3.0%
535
 
1.3%
635
 
1.3%
744
 
1.7%
857
 
2.2%
937
 
1.4%
ValueCountFrequency (%)
18002
0.1%
17002
0.1%
16001
< 0.1%
15002
0.1%
14001
< 0.1%
13002
0.1%
12001
< 0.1%
11002
0.1%
10001
< 0.1%
9571
< 0.1%

Alcohol
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct1032
Distinct (%)39.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.328683812
Minimum0
Maximum17.87
Zeros177
Zeros (%)6.7%
Negative0
Negative (%)0.0%
Memory size20.8 KiB
2022-05-01T20:28:01.141258image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.4075
median3.225
Q37.495
95-th percentile12
Maximum17.87
Range17.87
Interquartile range (IQR)7.0875

Descriptive statistics

Standard deviation4.146851238
Coefficient of variation (CV)0.9579935651
Kurtosis-0.7594094965
Mean4.328683812
Median Absolute Deviation (MAD)3.17
Skewness0.660487079
Sum11445.04
Variance17.19637519
MonotonicityNot monotonic
2022-05-01T20:28:01.263523image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01275
 
10.4%
0177
 
6.7%
0.0314
 
0.5%
0.0212
 
0.5%
0.0912
 
0.5%
0.2110
 
0.4%
0.559
 
0.3%
0.549
 
0.3%
0.569
 
0.3%
1.189
 
0.3%
Other values (1022)2108
79.7%
ValueCountFrequency (%)
0177
6.7%
0.01275
10.4%
0.0212
 
0.5%
0.0314
 
0.5%
0.046
 
0.2%
0.058
 
0.3%
0.068
 
0.3%
0.072
 
0.1%
0.088
 
0.3%
0.0912
 
0.5%
ValueCountFrequency (%)
17.871
< 0.1%
17.311
< 0.1%
16.991
< 0.1%
16.581
< 0.1%
16.351
< 0.1%
15.521
< 0.1%
15.191
< 0.1%
15.141
< 0.1%
15.071
< 0.1%
15.042
0.1%

percentage expenditure
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct2147
Distinct (%)81.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean792.4655522
Minimum0
Maximum19479.91161
Zeros498
Zeros (%)18.8%
Negative0
Negative (%)0.0%
Memory size20.8 KiB
2022-05-01T20:28:01.408213image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17.044564582
median70.64878737
Q3485.9595594
95-th percentile5066.826671
Maximum19479.91161
Range19479.91161
Interquartile range (IQR)478.9149948

Descriptive statistics

Standard deviation2077.768681
Coefficient of variation (CV)2.6219041
Kurtosis24.06125676
Mean792.4655522
Median Absolute Deviation (MAD)70.64878737
Skewness4.444776518
Sum2095278.92
Variance4317122.69
MonotonicityNot monotonic
2022-05-01T20:28:01.540310image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0498
 
18.8%
71.279623621
 
< 0.1%
274.54726051
 
< 0.1%
595.73069231
 
< 0.1%
527.30767211
 
< 0.1%
57.121901461
 
< 0.1%
495.07829631
 
< 0.1%
36.480240321
 
< 0.1%
33.669813551
 
< 0.1%
198.73434951
 
< 0.1%
Other values (2137)2137
80.8%
ValueCountFrequency (%)
0498
18.8%
0.099872191
 
< 0.1%
0.1080559731
 
< 0.1%
0.275648261
 
< 0.1%
0.3284180561
 
< 0.1%
0.3882537721
 
< 0.1%
0.3972287641
 
< 0.1%
0.53057281
 
< 0.1%
0.6615403711
 
< 0.1%
0.667515051
 
< 0.1%
ValueCountFrequency (%)
19479.911611
< 0.1%
19099.045061
< 0.1%
18961.34861
< 0.1%
18822.867321
< 0.1%
18379.329741
< 0.1%
17028.527981
< 0.1%
16255.161981
< 0.1%
15515.752341
< 0.1%
15345.49071
< 0.1%
15268.064451
< 0.1%

Hepatitis B
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct87
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.53290469
Minimum0
Maximum99
Zeros519
Zeros (%)19.6%
Negative0
Negative (%)0.0%
Memory size20.8 KiB
2022-05-01T20:28:01.664382image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q114.75
median87
Q396
95-th percentile99
Maximum99
Range99
Interquartile range (IQR)81.25

Descriptive statistics

Standard deviation39.22134736
Coefficient of variation (CV)0.5984985337
Kurtosis-1.018651157
Mean65.53290469
Median Absolute Deviation (MAD)11
Skewness-0.8708038193
Sum173269
Variance1538.314089
MonotonicityNot monotonic
2022-05-01T20:28:01.781512image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0519
19.6%
99223
 
8.4%
98195
 
7.4%
96155
 
5.9%
97141
 
5.3%
95134
 
5.1%
94111
 
4.2%
9394
 
3.6%
9269
 
2.6%
9167
 
2.5%
Other values (77)936
35.4%
ValueCountFrequency (%)
0519
19.6%
11
 
< 0.1%
24
 
0.2%
43
 
0.1%
57
 
0.3%
615
 
0.6%
717
 
0.6%
836
 
1.4%
951
 
1.9%
111
 
< 0.1%
ValueCountFrequency (%)
99223
8.4%
98195
7.4%
97141
5.3%
96155
5.9%
95134
5.1%
94111
4.2%
9394
3.6%
9269
 
2.6%
9167
 
2.5%
8967
 
2.5%

Measles
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct859
Distinct (%)32.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2432.922844
Minimum0
Maximum212183
Zeros899
Zeros (%)34.0%
Negative0
Negative (%)0.0%
Memory size20.8 KiB
2022-05-01T20:28:01.896177image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median14
Q3329.75
95-th percentile9561.55
Maximum212183
Range212183
Interquartile range (IQR)329.75

Descriptive statistics

Standard deviation11868.65244
Coefficient of variation (CV)4.878351349
Kurtosis110.7863047
Mean2432.922844
Median Absolute Deviation (MAD)14
Skewness9.361772676
Sum6432648
Variance140864910.7
MonotonicityNot monotonic
2022-05-01T20:28:02.009050image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0899
34.0%
198
 
3.7%
266
 
2.5%
341
 
1.6%
430
 
1.1%
629
 
1.1%
725
 
0.9%
525
 
0.9%
823
 
0.9%
1020
 
0.8%
Other values (849)1388
52.5%
ValueCountFrequency (%)
0899
34.0%
198
 
3.7%
266
 
2.5%
341
 
1.6%
430
 
1.1%
525
 
0.9%
629
 
1.1%
725
 
0.9%
823
 
0.9%
918
 
0.7%
ValueCountFrequency (%)
2121831
< 0.1%
1824851
< 0.1%
1681071
< 0.1%
1412581
< 0.1%
1338021
< 0.1%
1314411
< 0.1%
1242191
< 0.1%
1187121
< 0.1%
1109271
< 0.1%
1090231
< 0.1%

BMI
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct592
Distinct (%)22.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.66274584
Minimum0
Maximum87.3
Zeros34
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size20.8 KiB
2022-05-01T20:28:02.127584image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.1
Q118.9
median43
Q355.9
95-th percentile64.5
Maximum87.3
Range87.3
Interquartile range (IQR)37

Descriptive statistics

Standard deviation20.33975045
Coefficient of variation (CV)0.5400495901
Kurtosis-1.291098471
Mean37.66274584
Median Absolute Deviation (MAD)16.7
Skewness-0.2047583888
Sum99580.3
Variance413.7054485
MonotonicityNot monotonic
2022-05-01T20:28:02.240778image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
034
 
1.3%
58.516
 
0.6%
54.215
 
0.6%
59.914
 
0.5%
5714
 
0.5%
55.814
 
0.5%
59.413
 
0.5%
58.113
 
0.5%
52.813
 
0.5%
5511
 
0.4%
Other values (582)2487
94.1%
ValueCountFrequency (%)
034
1.3%
1.41
 
< 0.1%
1.81
 
< 0.1%
21
 
< 0.1%
2.111
 
0.4%
2.27
 
0.3%
2.36
 
0.2%
2.45
 
0.2%
2.58
 
0.3%
2.64
 
0.2%
ValueCountFrequency (%)
87.31
< 0.1%
83.31
< 0.1%
82.81
< 0.1%
81.61
< 0.1%
77.61
< 0.1%
77.31
< 0.1%
77.11
< 0.1%
76.71
< 0.1%
76.21
< 0.1%
75.71
< 0.1%

under-five deaths
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct240
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.16036309
Minimum0
Maximum2500
Zeros747
Zeros (%)28.3%
Negative0
Negative (%)0.0%
Memory size20.8 KiB
2022-05-01T20:28:02.361780image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q323
95-th percentile136
Maximum2500
Range2500
Interquartile range (IQR)23

Descriptive statistics

Standard deviation168.4859286
Coefficient of variation (CV)3.903718981
Kurtosis99.81352476
Mean43.16036309
Median Absolute Deviation (MAD)3
Skewness9.090007872
Sum114116
Variance28387.50814
MonotonicityNot monotonic
2022-05-01T20:28:02.476842image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0747
28.3%
1329
 
12.4%
2163
 
6.2%
4141
 
5.3%
3121
 
4.6%
1250
 
1.9%
640
 
1.5%
1038
 
1.4%
938
 
1.4%
535
 
1.3%
Other values (230)942
35.6%
ValueCountFrequency (%)
0747
28.3%
1329
12.4%
2163
 
6.2%
3121
 
4.6%
4141
 
5.3%
535
 
1.3%
640
 
1.5%
727
 
1.0%
835
 
1.3%
938
 
1.4%
ValueCountFrequency (%)
25001
< 0.1%
24001
< 0.1%
23001
< 0.1%
22001
< 0.1%
21001
< 0.1%
20002
0.1%
19001
< 0.1%
18001
< 0.1%
17001
< 0.1%
16001
< 0.1%

Polio
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct74
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean81.96709531
Minimum0
Maximum99
Zeros19
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size20.8 KiB
2022-05-01T20:28:02.592440image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9
Q178
median93
Q397
95-th percentile99
Maximum99
Range99
Interquartile range (IQR)19

Descriptive statistics

Standard deviation24.52152905
Coefficient of variation (CV)0.2991630843
Kurtosis3.408574123
Mean81.96709531
Median Absolute Deviation (MAD)6
Skewness-2.045952733
Sum216721
Variance601.3053868
MonotonicityNot monotonic
2022-05-01T20:28:02.709560image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99349
 
13.2%
98235
 
8.9%
97191
 
7.2%
96189
 
7.1%
95161
 
6.1%
94132
 
5.0%
93106
 
4.0%
9286
 
3.3%
9166
 
2.5%
8864
 
2.4%
Other values (64)1065
40.3%
ValueCountFrequency (%)
019
 
0.7%
37
 
0.3%
411
 
0.4%
58
 
0.3%
611
 
0.4%
721
 
0.8%
837
1.4%
960
2.3%
171
 
< 0.1%
231
 
< 0.1%
ValueCountFrequency (%)
99349
13.2%
98235
8.9%
97191
7.2%
96189
7.1%
95161
6.1%
94132
 
5.0%
93106
 
4.0%
9286
 
3.3%
9166
 
2.5%
8952
 
2.0%

Total expenditure
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct796
Distinct (%)30.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.431077912
Minimum0
Maximum17.24
Zeros208
Zeros (%)7.9%
Negative0
Negative (%)0.0%
Memory size20.8 KiB
2022-05-01T20:28:02.823775image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.6775
median5.55
Q37.3725
95-th percentile9.6485
Maximum17.24
Range17.24
Interquartile range (IQR)3.695

Descriptive statistics

Standard deviation2.821981619
Coefficient of variation (CV)0.5195988099
Kurtosis-0.1416073664
Mean5.431077912
Median Absolute Deviation (MAD)1.85
Skewness-0.03049886366
Sum14359.77
Variance7.96358026
MonotonicityNot monotonic
2022-05-01T20:28:02.940904image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0208
 
7.9%
4.614
 
0.5%
6.711
 
0.4%
3.410
 
0.4%
5.610
 
0.4%
5.259
 
0.3%
9.19
 
0.3%
5.829
 
0.3%
6.19
 
0.3%
8.98
 
0.3%
Other values (786)2347
88.8%
ValueCountFrequency (%)
0208
7.9%
0.371
 
< 0.1%
0.651
 
< 0.1%
0.741
 
< 0.1%
0.761
 
< 0.1%
0.921
 
< 0.1%
1.12
 
0.1%
1.123
 
0.1%
1.152
 
0.1%
1.172
 
0.1%
ValueCountFrequency (%)
17.241
< 0.1%
14.391
< 0.1%
13.831
< 0.1%
13.761
< 0.1%
13.731
< 0.1%
13.711
< 0.1%
13.661
< 0.1%
13.631
< 0.1%
13.441
< 0.1%
13.381
< 0.1%

Diphtheria
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct82
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean81.92662632
Minimum0
Maximum99
Zeros19
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size20.8 KiB
2022-05-01T20:28:03.064713image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9
Q178
median93
Q397
95-th percentile99
Maximum99
Range99
Interquartile range (IQR)19

Descriptive statistics

Standard deviation24.50277201
Coefficient of variation (CV)0.2990819116
Kurtosis3.340316386
Mean81.92662632
Median Absolute Deviation (MAD)6
Skewness-2.041433798
Sum216614
Variance600.3858364
MonotonicityNot monotonic
2022-05-01T20:28:03.184435image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99327
 
12.4%
98233
 
8.8%
97190
 
7.2%
96174
 
6.6%
95173
 
6.5%
94128
 
4.8%
93115
 
4.3%
9287
 
3.3%
9179
 
3.0%
8973
 
2.8%
Other values (72)1065
40.3%
ValueCountFrequency (%)
019
 
0.7%
21
 
< 0.1%
34
 
0.2%
412
 
0.5%
58
 
0.3%
616
 
0.6%
717
 
0.6%
837
1.4%
951
1.9%
161
 
< 0.1%
ValueCountFrequency (%)
99327
12.4%
98233
8.8%
97190
7.2%
96174
6.6%
95173
6.5%
94128
 
4.8%
93115
 
4.3%
9287
 
3.3%
9179
 
3.0%
8973
 
2.8%

HIV/AIDS
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct177
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.61709531
Minimum0.1
Maximum50.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.8 KiB
2022-05-01T20:28:03.301531image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.1
Q10.1
median0.1
Q30.8
95-th percentile7.285
Maximum50.6
Range50.5
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation4.82215012
Coefficient of variation (CV)2.981982626
Kurtosis40.63260529
Mean1.61709531
Median Absolute Deviation (MAD)0
Skewness5.803790413
Sum4275.6
Variance23.25313178
MonotonicityNot monotonic
2022-05-01T20:28:03.425534image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.11600
60.5%
0.2106
 
4.0%
0.3105
 
4.0%
0.464
 
2.4%
0.540
 
1.5%
0.634
 
1.3%
0.830
 
1.1%
0.928
 
1.1%
0.726
 
1.0%
1.621
 
0.8%
Other values (167)590
 
22.3%
ValueCountFrequency (%)
0.11600
60.5%
0.2106
 
4.0%
0.3105
 
4.0%
0.464
 
2.4%
0.540
 
1.5%
0.634
 
1.3%
0.726
 
1.0%
0.830
 
1.1%
0.928
 
1.1%
110
 
0.4%
ValueCountFrequency (%)
50.61
< 0.1%
50.31
< 0.1%
49.91
< 0.1%
49.11
< 0.1%
48.81
< 0.1%
46.41
< 0.1%
43.71
< 0.1%
40.71
< 0.1%
40.21
< 0.1%
38.81
< 0.1%

GDP
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct2293
Distinct (%)86.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6726.080237
Minimum0
Maximum119172.7418
Zeros352
Zeros (%)13.3%
Negative0
Negative (%)0.0%
Memory size20.8 KiB
2022-05-01T20:28:03.553308image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1238.6268817
median1254.65281
Q35277.746332
95-th percentile38527.25734
Maximum119172.7418
Range119172.7418
Interquartile range (IQR)5039.119451

Descriptive statistics

Standard deviation13873.88146
Coefficient of variation (CV)2.062699369
Kurtosis13.782567
Mean6726.080237
Median Absolute Deviation (MAD)1254.65281
Skewness3.390310702
Sum17783756.15
Variance192484586.8
MonotonicityNot monotonic
2022-05-01T20:28:03.690094image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0352
 
13.3%
584.259211
 
< 0.1%
216.2432741
 
< 0.1%
1627.428931
 
< 0.1%
2847.2855691
 
< 0.1%
3154.5134841
 
< 0.1%
3111.7628871
 
< 0.1%
294.7467281
 
< 0.1%
339.916161
 
< 0.1%
2834.24721
 
< 0.1%
Other values (2283)2283
86.3%
ValueCountFrequency (%)
0352
13.3%
1.681351
 
< 0.1%
3.6859491
 
< 0.1%
4.61357451
 
< 0.1%
5.66872641
 
< 0.1%
8.3764321
 
< 0.1%
11.1472771
 
< 0.1%
11.336781
 
< 0.1%
11.5531961
 
< 0.1%
11.6313771
 
< 0.1%
ValueCountFrequency (%)
119172.74181
< 0.1%
115761.5771
< 0.1%
114293.84331
< 0.1%
113751.851
< 0.1%
89739.71171
< 0.1%
88564.822981
< 0.1%
87998.444681
< 0.1%
87646.753461
< 0.1%
86852.71191
< 0.1%
85948.7461
< 0.1%

Population
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct2098
Distinct (%)79.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10521518.87
Minimum0
Maximum1293859294
Zeros540
Zeros (%)20.4%
Negative0
Negative (%)0.0%
Memory size20.8 KiB
2022-05-01T20:28:03.812297image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q112859
median622796.5
Q35194998.75
95-th percentile42182320.9
Maximum1293859294
Range1293859294
Interquartile range (IQR)5182139.75

Descriptive statistics

Standard deviation56809774.24
Coefficient of variation (CV)5.399389096
Kurtosis345.7775414
Mean10521518.87
Median Absolute Deviation (MAD)622796.5
Skewness17.1560811
Sum2.781889588 × 1010
Variance3.227350449 × 1015
MonotonicityNot monotonic
2022-05-01T20:28:03.931568image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0540
 
20.4%
4444
 
0.2%
1274452
 
0.1%
11412
 
0.1%
2922
 
0.1%
7182392
 
0.1%
319898971
 
< 0.1%
32496391
 
< 0.1%
315968551
 
< 0.1%
328588231
 
< 0.1%
Other values (2088)2088
79.0%
ValueCountFrequency (%)
0540
20.4%
341
 
< 0.1%
361
 
< 0.1%
411
 
< 0.1%
431
 
< 0.1%
1231
 
< 0.1%
1351
 
< 0.1%
2861
 
< 0.1%
2922
 
0.1%
2971
 
< 0.1%
ValueCountFrequency (%)
12938592941
< 0.1%
11796812391
< 0.1%
11619777191
< 0.1%
11441186741
< 0.1%
11261357771
< 0.1%
2581621131
< 0.1%
2551311161
< 0.1%
2488832321
< 0.1%
2425241231
< 0.1%
2361592761
< 0.1%

thinness 10-19 years
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct198
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.743910741
Minimum0
Maximum27.7
Zeros34
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size20.8 KiB
2022-05-01T20:28:04.049451image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.415
Q11.5
median3.2
Q37.2
95-th percentile13.3
Maximum27.7
Range27.7
Interquartile range (IQR)5.7

Descriptive statistics

Standard deviation4.467839082
Coefficient of variation (CV)0.9418050477
Kurtosis4.220610583
Mean4.743910741
Median Absolute Deviation (MAD)2.3
Skewness1.763873058
Sum12542.9
Variance19.96158606
MonotonicityNot monotonic
2022-05-01T20:28:04.160131image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
174
 
2.8%
1.965
 
2.5%
1.261
 
2.3%
2.161
 
2.3%
2.258
 
2.2%
257
 
2.2%
0.957
 
2.2%
1.153
 
2.0%
1.352
 
2.0%
0.851
 
1.9%
Other values (188)2055
77.7%
ValueCountFrequency (%)
034
1.3%
0.123
0.9%
0.239
1.5%
0.332
1.2%
0.45
 
0.2%
0.535
1.3%
0.641
1.6%
0.744
1.7%
0.851
1.9%
0.957
2.2%
ValueCountFrequency (%)
27.71
 
< 0.1%
27.51
 
< 0.1%
27.41
 
< 0.1%
27.31
 
< 0.1%
27.22
0.1%
27.12
0.1%
273
0.1%
26.92
0.1%
26.82
0.1%
26.71
 
< 0.1%

thinness 5-9 years
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct204
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.778214826
Minimum0
Maximum28.6
Zeros34
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size20.8 KiB
2022-05-01T20:28:04.272559image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.3
Q11.5
median3.2
Q37.3
95-th percentile13.6
Maximum28.6
Range28.6
Interquartile range (IQR)5.8

Descriptive statistics

Standard deviation4.553417977
Coefficient of variation (CV)0.9529538003
Kurtosis4.632168812
Mean4.778214826
Median Absolute Deviation (MAD)2.3
Skewness1.831556234
Sum12633.6
Variance20.73361528
MonotonicityNot monotonic
2022-05-01T20:28:04.387533image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.969
 
2.6%
1.167
 
2.5%
1.963
 
2.4%
162
 
2.3%
2.161
 
2.3%
1.358
 
2.2%
252
 
2.0%
2.549
 
1.9%
0.548
 
1.8%
1.747
 
1.8%
Other values (194)2068
78.2%
ValueCountFrequency (%)
034
1.3%
0.131
1.2%
0.245
1.7%
0.325
 
0.9%
0.417
 
0.6%
0.548
1.8%
0.638
1.4%
0.745
1.7%
0.836
1.4%
0.969
2.6%
ValueCountFrequency (%)
28.61
< 0.1%
28.51
< 0.1%
28.41
< 0.1%
28.31
< 0.1%
28.21
< 0.1%
28.11
< 0.1%
282
0.1%
27.91
< 0.1%
27.82
0.1%
27.71
< 0.1%

Income composition of resources
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct622
Distinct (%)23.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6028789713
Minimum0
Maximum0.948
Zeros230
Zeros (%)8.7%
Negative0
Negative (%)0.0%
Memory size20.8 KiB
2022-05-01T20:28:04.507879image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.473
median0.668
Q30.781
95-th percentile0.89285
Maximum0.948
Range0.948
Interquartile range (IQR)0.308

Descriptive statistics

Standard deviation0.244039778
Coefficient of variation (CV)0.4047906623
Kurtosis0.7025925394
Mean0.6028789713
Median Absolute Deviation (MAD)0.145
Skewness-1.110901853
Sum1594.012
Variance0.05955541327
MonotonicityNot monotonic
2022-05-01T20:28:04.616635image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0230
 
8.7%
0.715
 
0.6%
0.73912
 
0.5%
0.63612
 
0.5%
0.73511
 
0.4%
0.79711
 
0.4%
0.70311
 
0.4%
0.87711
 
0.4%
0.68611
 
0.4%
0.8611
 
0.4%
Other values (612)2309
87.3%
ValueCountFrequency (%)
0230
8.7%
0.2531
 
< 0.1%
0.2551
 
< 0.1%
0.2611
 
< 0.1%
0.2661
 
< 0.1%
0.2683
 
0.1%
0.271
 
< 0.1%
0.2761
 
< 0.1%
0.2781
 
< 0.1%
0.2791
 
< 0.1%
ValueCountFrequency (%)
0.9481
 
< 0.1%
0.9451
 
< 0.1%
0.9421
 
< 0.1%
0.9411
 
< 0.1%
0.9391
 
< 0.1%
0.9381
 
< 0.1%
0.9371
 
< 0.1%
0.9365
0.2%
0.9342
 
0.1%
0.9331
 
< 0.1%

Schooling
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct173
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.48267776
Minimum0
Maximum20.7
Zeros141
Zeros (%)5.3%
Negative0
Negative (%)0.0%
Memory size20.8 KiB
2022-05-01T20:28:04.730871image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q19.5
median12.3
Q314.2
95-th percentile16.9
Maximum20.7
Range20.7
Interquartile range (IQR)4.7

Descriptive statistics

Standard deviation4.170684252
Coefficient of variation (CV)0.3632153004
Kurtosis1.064846057
Mean11.48267776
Median Absolute Deviation (MAD)2.3
Skewness-0.9793273376
Sum30360.2
Variance17.39460713
MonotonicityNot monotonic
2022-05-01T20:28:04.840707image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0141
 
5.3%
12.956
 
2.1%
13.344
 
1.7%
12.844
 
1.7%
12.541
 
1.6%
12.639
 
1.5%
15.838
 
1.4%
12.338
 
1.4%
12.438
 
1.4%
12.737
 
1.4%
Other values (163)2128
80.5%
ValueCountFrequency (%)
0141
5.3%
2.81
 
< 0.1%
2.94
 
0.2%
31
 
< 0.1%
3.11
 
< 0.1%
3.31
 
< 0.1%
3.41
 
< 0.1%
3.53
 
0.1%
3.61
 
< 0.1%
3.72
 
0.1%
ValueCountFrequency (%)
20.71
 
< 0.1%
20.61
 
< 0.1%
20.51
 
< 0.1%
20.43
0.1%
20.34
0.2%
20.12
0.1%
19.81
 
< 0.1%
19.71
 
< 0.1%
19.53
0.1%
19.32
0.1%

Interactions

2022-05-01T20:27:57.169885image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:12.775937image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:14.968037image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:17.061717image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:19.330535image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:21.602049image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:23.729217image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:26.110667image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:28.251521image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:30.766913image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:33.308795image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:35.858880image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:38.105529image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:40.484633image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:42.494092image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:44.480459image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:47.392854image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:50.069622image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:52.604047image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:55.048859image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:57.268382image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:12.890478image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:15.068065image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:17.174859image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:19.434616image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:21.709476image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:23.836368image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:26.218047image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:28.368015image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:30.869214image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:33.468183image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:35.961407image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:38.211659image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:40.588123image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:42.589137image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:44.583116image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:47.538197image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:50.171548image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:52.752002image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:55.153743image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:57.713495image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:13.077428image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:15.164027image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:17.276756image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:19.536139image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:21.809769image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:23.942208image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:26.315239image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:28.474582image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:30.964906image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:33.610537image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:36.057889image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:38.320098image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:40.681420image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:42.683212image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:44.678396image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:47.678283image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:50.299958image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:52.880527image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:55.255168image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:57.822687image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:13.193401image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:15.354845image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:17.387913image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:19.647440image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:21.921700image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:24.058174image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:26.418430image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:28.589929image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:31.069166image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:33.740130image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:36.165766image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:38.667355image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:40.787370image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:42.780695image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:44.788132image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:47.831944image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:50.445124image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:52.992739image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:55.367093image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:57.930396image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:13.299785image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:15.464493image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:17.497740image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:19.760423image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:22.033066image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:24.173913image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:26.526014image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:28.705976image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:31.401348image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:33.846893image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:36.275686image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:38.781373image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:40.892843image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:42.881406image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:44.891907image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:47.984287image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:50.589413image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:53.142044image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:55.480039image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:58.036138image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:13.411012image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:15.571726image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:17.608491image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:19.870903image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:22.142641image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:24.284390image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:26.630606image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:28.851556image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:31.510348image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:33.959907image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:36.420785image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:38.892948image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:40.998432image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:42.987834image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:44.997054image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:48.141132image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:50.706324image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:53.272701image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:55.589061image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:58.145006image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:13.527892image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:15.681797image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:17.725686image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:19.987092image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:22.255936image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:24.404889image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:26.742567image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:29.037426image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:31.615340image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:34.078532image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:36.580393image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:39.005702image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:41.108603image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:43.097212image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:45.110778image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:48.300032image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-05-01T20:27:59.445915image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:14.872539image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:16.961412image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:19.228360image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:21.491821image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:23.625368image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:25.997574image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:28.130300image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:30.646839image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:33.197222image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:35.753957image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:38.004791image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:40.368983image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:42.401403image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:44.384605image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:47.262264image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:49.969358image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:52.466919image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:54.934410image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T20:27:57.067105image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-05-01T20:28:04.956812image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-05-01T20:28:05.151186image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-05-01T20:28:05.344760image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-05-01T20:28:05.544229image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-05-01T20:27:59.624133image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-05-01T20:27:59.946084image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

Unnamed: 0Life expectancyAdult Mortalityinfant deathsAlcoholpercentage expenditureHepatitis BMeaslesBMIunder-five deathsPolioTotal expenditureDiphtheriaHIV/AIDSGDPPopulationthinness 10-19 yearsthinness 5-9 yearsIncome composition of resourcesSchooling
0065.0263.0620.0171.27962465.0115419.1836.08.1665.00.1584.25921033736494.017.217.30.47910.1
1159.9271.0640.0173.52358262.049218.68658.08.1862.00.1612.696514327582.017.517.50.47610.0
2259.9268.0660.0173.21924364.043018.18962.08.1364.00.1631.74497631731688.017.717.70.4709.9
3359.5272.0690.0178.18421567.0278717.69367.08.5267.00.1669.9590003696958.017.918.00.4639.8
4459.2275.0710.017.09710968.0301317.29768.07.8768.00.163.5372312978599.018.218.20.4549.5
5558.8279.0740.0179.67936766.0198916.710266.09.2066.00.1553.3289402883167.018.418.40.4489.2
6658.6281.0770.0156.76221763.0286116.210663.09.4263.00.1445.893298284331.018.618.70.4348.9
7758.1287.0800.0325.87392564.0159915.711064.08.3364.00.1373.3611162729431.018.818.90.4338.7
8857.5295.0820.0210.91015663.0114115.211363.06.7363.00.1369.83579626616792.019.019.10.4158.4
9957.3295.0840.0317.17151864.0199014.711658.07.4358.00.1272.5637702589345.019.219.30.4058.1

Last rows

Unnamed: 0Life expectancyAdult Mortalityinfant deathsAlcoholpercentage expenditureHepatitis BMeaslesBMIunder-five deathsPolioTotal expenditureDiphtheriaHIV/AIDSGDPPopulationthinness 10-19 yearsthinness 5-9 yearsIncome composition of resourcesSchooling
2634263473.3135.000.01565.9672178.0074.8082.05.188.00.14192.34975815782.00.10.10.71614.3
2635263573.2137.000.01584.94498982.0074.3084.04.9882.00.14266.55717415328.00.10.10.71814.3
2636263673.0138.000.0163.80295077.0073.8079.04.5177.00.1451.54246214951.00.10.10.71714.4
2637263772.914.000.967.03398182.0073.3084.04.6082.00.1445.18866014577.00.10.10.71214.4
2638263872.8142.001.24471.83076782.0072.7084.04.5982.00.13547.59975014137.00.10.10.70714.4
2639263972.5147.001.084.78380684.0072.1086.04.6784.00.137.8465631364.00.10.10.70314.5
2640264072.6145.001.10569.62550484.0071.5088.05.1386.00.13392.647430135.00.10.10.69814.5
2641264172.5146.002.05568.86928187.007.8088.05.8287.00.12932.31588312357.00.10.10.69814.5
2642264272.4148.001.79503.58819689.007.1089.05.6188.00.12892.52266311689.00.10.10.69514.6
2643264372.315.001.57689.94402289.0069.409.06.5289.00.12594.7499901141.00.10.10.69414.6